Estimates of many ecosystem service flows are sensitive to the scale at which they are measured. It is important to choose the appropriate resolution of input data.

Comparisons of different methods for measuring vegetation using LiDAR revealed that the most commonly used method could be improved.

The ESI-SIT tool can be used to visualise how different management interventions are likely to change interactions in ecosystem services and is useful for decision-support.

The Saltmarsh Carbon StockPredictor uses simple information about vegetation and soil type to predict the quantity and distribution of carbon stored in saltmarshes.

The Saltmarsh App increases awareness of the importance of saltmarsh and enables anyone to contribute to improved carbon stock prediction.

The Ecosystem Approach aims to involve people in decisions that affect how we use land, so that a full range of ecosystem services can be maintained for society now and in an uncertain future. This means that a wide variety of people and organisations need information about the location and condition of natural resources. Mapping tools can be used in a variety of ways to explore the likely implications of decisions on ecosystem services. BESS research is providing greater access to information on mapping ecosystem services, improving the use of existing mapping tools and developing new tools.

Mapping the maps – accessing information and choosing tools.

The Ecosystem Services Mapping Gateway

There is growing interest in mapping ecosystem services at a landscape scale as part of implementing the Ecosystem Approach. It is important that new initiatives are able to collaborate with and learn from existing projects. The BESS hosted Ecosystem Services Mapping Gateway provides an interactive way to explore projects and to share good practice. A key decision is the choice or development of mapping tools that are appropriate for the project aims. Mapping tools use Geographical Information System to link habitats to the resources and ecosystem services they provide. They can be particularly useful in the early involvement of stakeholders in clarifying the implications and trade-offs associated with particular decisions about land use. There are a wide variety of open-access mapping tools available that vary in approach and will be appropriate for different needs. Some tools simply show the relative importance of different land types for proving different services and may have been developed for particular local uses only. Others are more sophisticated process-based models that consider a wide range of services at different scales. BESS also part-funds the Ecosystems Knowledge Network (EKN) and their Tool Assessor provides information about a variety of systems that analyse ecosystem services, including mapping tools. The EKN also points to a number of guidance tools to support projects that are mapping ecosystem services.

Improving the use of existing modelling tools

ARIES, InVEST and LUCI are models that can be used to aid understanding of the interactions and flows of multiple ecosystem services across the landscape to people. BESS researchers have been using and contributing to the further development of these tools. These models treat the landscape in different ways and users need clarity on the implications that their choice of modelling approach has for different needs and scales. Ongoing BESS research by Laurence Jones and colleagues is comparing the three models when investigating how landscape structure influences the delivery of multiple ecosystem services.

Mapping ecosystem stocks and services in urban areas

F3UES mapping and modelling, Karen Anderson

Modelling ecosystem services using tools such as InVEST first requires knowledge about where different types of landcover are in your area of interest. Full waveform aerial laser scanning (LiDAR) is a valuable tool for surveying that can be used to provide a wide variety of information, such as the 3D structure and density of vegetation. Steven Hancock and colleagues from the F3UES consortium have developed a set of open source tools for mapping vegetation with waveform lidar data in far more detail than has previously been possible [1, 2].

Karen Anderson’s team have been visualising the greenspace in Bedford, Milton Keynes and Luton using the output of the open source LiDAR tools [2] to build minecraft and physical models. Removing all the buildings and seeing greenspace in 3D improves understanding of the way in which wildlife might move through urban space.

Maps of landcover vary in spatial resolution (pixel size); moderate to coarse resolutions (> 20 m) may be more readily available and cheaper than those at fine resolutions (5 m). Particular habitat features can be more important for modelling some ecosystem services than others and small areas of habitat can be obscured at coarser scales. Users of models need to understand how sensitive their results are to the spatial resolution of the information they are using.

Darren Grafius and colleagues tested this by modelling ecosystem services in the three F3UES cities using landcover maps at 5 m and at 25 m resolution [3]. They were interested in whether the services of carbon storage, sediment erosion and pollination are affected in the same way by these different resolutions. Outputs were sensitive to spatial scale and this varied with the type of service being modelled. Finer scale resolution data resulted in higher estimates of carbon storage and lower sediment erosion, but lower pollination provision. Care needs to be taken to select data at the correct resolution for the question being asked. Given the complexity of urban areas, it might be helpful to use finer scale mapping than is normally used in rural areas.

Many mapping tools used to explore interactions between ecosystem services need levels of training and data that are not possible or appropriate for many users. BESS and Defra funded ESI-SIT (Ecosystem Service Interactions – Spatial Interactive Tool) has been developed with participation from stakeholders who make decisions about land management. This web-based tool allows the user to visualise likely changes in ecosystem service interactions across the landscape as a result of selecting different management interventions. Importantly, it also includes a simple representation of levels of uncertainty and provides links to the evidence underlying the predictions. The versions currently available are for the Humberhead Levels NIA and the Tees Valley.
Mapping carbon storage in saltmarshes
Saltmarshes store large amounts of carbon. Land management and policy development that aims to support natural carbon capture and storage needs to know what is stored where. However, there is currently little information on carbon stocks in British saltmarshes and carrying out new sampling is costly. The CBESS research consortium have shown how saltmarsh vegetation and soil type can be used to predict the quantity and distribution of carbon in English and Welsh saltmarshes [4]. The information about vegetation and soil can be obtained from simple measurements or existing data. CBESS have produced two tools that can be used by non-specialists: The Saltmarsh Carbon Stock Predictor and The Saltmarsh App.

The Saltmarsh Carbon Stock Predictor

The Saltmarsh Carbon Stock Predictor is aimed at government and non-government organisations. The user manual provides more information about the evidence underpinning the tool and app [4]. The development of the tool was informed by collaboration with Natural Resources Wales. Detailed maps of predicted carbon storage in Welsh saltmarshes are available from the team.

The Saltmarsh App

The Saltmarsh App is a citizen science tool that enables anyone to carry out simple surveys and contribute information on vegetation and soil type in saltmarshes. These surveys provide information that can be used to improve carbon stock predictions. The Saltmarsh App also helps to increase awareness about the benefits we get from coastal ecosystems, provides information about coastal biodiversity and encourages enjoyment of and connection with nature.